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This article is part of our Opinions section, where we invite industry professionals to share their views on the most pressing technology questions of our time.
Behind every clinical decision – whether diagnosing a patient’s illness, shaping the most suitable care pathways, or determining when to intensify life-sustaining treatments – is a moment that rarely features on a digital roadmap. A clinician pauses, looks at the information presented to them on screen, interprets what they see, and decides on the most appropriate next step.
While brief, this interaction between human and data is essential in determining the success of digital healthcare. One overlooked lab result or misinterpreted imaging report could be the difference between reaching an optimal clinical outcome and facing a missed opportunity for timely intervention – or providing an incorrect diagnosis altogether.
Over the last decade, the NHS has made significant strides in digitisation. Paper records continue to be replaced with electronic alternatives, scans and documents are accessible at the click of a button, and data volumes have grown exponentially, providing clinicians with more detailed insights to base care decisions on. Yet, despite such fruitful progress, one critical detail remains overlooked. While information is abundant, it is not always entirely helpful.
Systems can be clunky and complex to navigate, risking fragmented clinical attention and increased cognitive burden, while data is often unstructured and therefore difficult to interpret, prioritise, and synthesise into a coherent clinical picture. This is the forgotten interface of technology, and where the promise of digital efficiency can get lost: not in the software itself, but in how it supports clinicians in thinking more holistically with data.
Clinicians do not consume data in neat, linear ways. They skim, compare, cross-reference, and check information as they move along. They look for patterns, anomalies, and reassurance. They ask questions: What is this document showing me right now? Has anything changed since the last report? Can I be confident in what I’m seeing?
When systems are designed primarily around data capture or regulatory compliance, this cognitive reality is often overlooked. Information may be technically accurate and complete, but still difficult to interpret successfully under pressure. A blood result buried three screens deep or a scanned doctor’s note presented without clear context, for example, creates friction at the most pivotal moment – the point of patient care.
Trust plays an important role here, too. Clinicians build confidence in systems that consistently surface the right information at the right time, because it streamlines the very first step of decision making, while proving the systems they use daily can be relied upon for total technical accuracy. Ultimately, this guarantee of factual correctness is not only essential for building professional credibility but also for ensuring patient outcomes are genuinely the best fit.
When data feels disconnected, poorly organised, or overwhelming to interpret, it can be difficult to earn that trust. As a result, trusts may experience hesitation from clinicians, process workarounds, or even a return to familiar habits that bypass digital tools altogether. Good digital design does not alter the clinician’s thinking process – it productively supports it.
One of the biggest ironies of healthcare digitisation is that having “more data” can make decisions harder, not easier. Today’s clinicians face an unprecedented volume of information (which is constantly growing), and they’re often presented with little guidance on prioritisation. Every alert competes for attention. From low-risk, soft-stop prompts – such as advisory prescribing alerts or documentation update reminders – to high-severity, hard-stop interventions that halt workflows until addressed, clinicians are required to constantly assess urgency, make rapid judgment calls, and decide where best to focus their time and attention.
This is one of the biggest drivers of cognitive fatigue for clinicians. Context switching between systems, windows, and workflows drains mental energy, which, over time, can dull situational awareness and slow critical decision-making. This is not the result of skills gaps or dwindling engagement on the part of clinicians, but an inherent design problem that fails to account for how clinicians process, filter, and act on information under real-world conditions.
In such circumstances, the burden shifts from the system to the user to make sense of the chaos. In high-pressure environments, that burden can be significant. Yes, efficiency is about speed. However, it is also about reducing unnecessary mental effort so attention can stay focused on the patient at all times. The suggestion is not to strip away data. Instead, it is to present it with intention, structuring and surfacing information in ways that support clinical reasoning and enable clinicians to think more confidently and clearly.
Context-aware design recognises that relevance changes depending on who is looking at medical records, why they are looking, and what decision they might be trying to make, bringing related information together and highlighting what is most likely to matter.
Achieving this does not require complex jargon or endless technology integrations. Often, it is about thoughtful organisation, clear visual cues, and workflows that align with real clinical practice. When done well, the technology fades into the background, and the clinician can focus on care and considered thought processes, rather than time-inducing manual navigation.
Carefully designed electronic document management systems (EDMS) have shown how consolidating and presenting records in a clear and contextual way – often alongside existing electronic patient records (EPRs) – can deliver transformative value for clinicians, driving efficiency and structure in day-to-day workloads without demanding radical behavioural change.
Beyond costs, healthcare technology conversations often focus on usability, from implementing fewer clicks and creating faster log-in processes to streamlining navigation and reducing unnecessary task repetition. These things matter, but they are only part of the picture.
One of the greatest opportunities lies in decision intelligence. More than making data easier to access, this is about making data easier to think with – able to be used intuitively, easily interpreted, and productively leveraged to influence improved patient outcomes. It acknowledges that clinical decisions are not only complex, but typically time sensitive and rarely made in isolation.
Successful decision intelligence, therefore, hinges significantly on information being complete and connected. When patient data is fragmented across departments, systems, or organisations, clinicians are forced to make decisions based on partial views of the patient’s story. The continuity of interoperable platforms supports more coordinated decision making, reduces duplication, and enables faster, more confident clinical action throughout the NHS.
Designing interfaces that reflect clinical logic rather than technical structures is equally key. This means helping users quickly build a mental model of the patient’s story, rather than piecing it together from disconnected fragments. In practice, this may include the careful use of AI to organise and contextualise information – not to make decisions on behalf of clinicians, but to support clearer human judgment. It also means recognising that confidence is a legitimate outcome of good digital design. When clinicians feel supported by their systems, decision-making becomes more consistent, communication improves, and the risk of error decreases.
As the NHS’s digital journey continues at pace, the temptation will be to measure progress in terms of systems deployed and data collected. While those metrics are important in understanding the scale and technical capability of digital systems, they fail to offer a complete view of how effectively technology supports clinical decision-making in practice.
Suppliers and partners have a central responsibility here. Providing more data, features, or dashboards is not enough. The real test is whether solutions create more productive and empowering interactions between humans and data. This requires listening directly to clinicians, observing how systems are used in situ, and valuing simplicity at every step. It also requires humility: accepting that the most impactful innovation may be subtle, even invisible, to anyone not using the system day in and day out.
The future of digital healthcare will not be decided solely by algorithms, infrastructure, or the volume of data available at trusts’ disposal. Instead, it will be shaped in thousands of quiet yet powerful moments, when a clinician looks at a screen and decides exactly what to do next.
If we’re eager to empower better patient outcomes in the NHS, we must pay attention to those instances. The forgotten interface is not a technical problem to be solved once, but a human experience to be continually refined as clinical practice, patient needs, and digital capabilities evolve. When we design digital systems based on how clinicians think and work with data – and not simply how they find and store it – digitisation will finally deliver on its promise.
